Update app.py

app.py

@@ -1,314 +1,306 @@
-```python
-# Gradio UI aligned to the training script column names (October1.xlsx)
-# - Uses the trained pipeline saved as: stress_gf_xgb.joblib
-# - Makes many inputs optional; missing values are handled by the pipeline imputers
-
 import os
 import joblib
 import numpy as np
 import pandas as pd
 import gradio as gr
 
-# ========================= Column Names (match training script) =========================
+========================= Column Names (match training script) =========================
 
 CF_COL = "Conductive Filler Conc. (wt%)"
 TARGET_COL = "Stress GF (MPa-1)"
 
 MAIN_VARIABLES = [
-    "Filler 1 Type",
-    "Filler 1 Diameter (µm)",
-    "Filler 1 Length (mm)",
-    CF_COL,
-    "Filler 1 Dimensionality",
-    "Filler 2 Type",
-    "Filler 2 Diameter (µm)",
-    "Filler 2 Length (mm)",
-    "Filler 2 Dimensionality",
-    "Specimen Volume (mm3)",
-    "Probe Count",
-    "Probe Material",
-    "W/B",
-    "S/B",
-    "Gauge Length (mm)",
-    "Curing Condition",
-    "Number of Fillers",
-    "Drying Temperature (°C)",
-    "Drying Duration (hr)",
-    "Loading Rate (MPa/s)",
-    "Modulus of Elasticity (GPa)",
-    "Current Type",
-    "Applied Voltage (V)"
+"Filler 1 Type",
+"Filler 1 Diameter (µm)",
+"Filler 1 Length (mm)",
+CF_COL,
+"Filler 1 Dimensionality",
+"Filler 2 Type",
+"Filler 2 Diameter (µm)",
+"Filler 2 Length (mm)",
+"Filler 2 Dimensionality",
+"Specimen Volume (mm3)",
+"Probe Count",
+"Probe Material",
+"W/B",
+"S/B",
+"Gauge Length (mm)",
+"Curing Condition",
+"Number of Fillers",
+"Drying Temperature (°C)",
+"Drying Duration (hr)",
+"Loading Rate (MPa/s)",
+"Modulus of Elasticity (GPa)",
+"Current Type",
+"Applied Voltage (V)"
 ]
 
 NUMERIC_COLS = {
-    "Filler 1 Diameter (µm)",
-    "Filler 1 Length (mm)",
-    CF_COL,
-    "Filler 2 Diameter (µm)",
-    "Filler 2 Length (mm)",
-    "Specimen Volume (mm3)",
-    "Probe Count",
-    "W/B",
-    "S/B",
-    "Gauge Length (mm)",
-    "Number of Fillers",
-    "Drying Temperature (°C)",
-    "Drying Duration (hr)",
-    "Loading Rate (MPa/s)",
-    "Modulus of Elasticity (GPa)",
-    "Applied Voltage (V)"
+"Filler 1 Diameter (µm)",
+"Filler 1 Length (mm)",
+CF_COL,
+"Filler 2 Diameter (µm)",
+"Filler 2 Length (mm)",
+"Specimen Volume (mm3)",
+"Probe Count",
+"W/B",
+"S/B",
+"Gauge Length (mm)",
+"Number of Fillers",
+"Drying Temperature (°C)",
+"Drying Duration (hr)",
+"Loading Rate (MPa/s)",
+"Modulus of Elasticity (GPa)",
+"Applied Voltage (V)"
 }
 
 CATEGORICAL_COLS = {
-    "Filler 1 Type",
-    "Filler 1 Dimensionality",
-    "Filler 2 Type",
-    "Filler 2 Dimensionality",
-    "Probe Material",
-    "Curing Condition",
-    "Current Type"
+"Filler 1 Type",
+"Filler 1 Dimensionality",
+"Filler 2 Type",
+"Filler 2 Dimensionality",
+"Probe Material",
+"Curing Condition",
+"Current Type"
 }
 
-# Reasonable UI choices (free text is still allowed)
+Reasonable UI choices (free text is still allowed)
+
 DIM_CHOICES = ["0D", "1D", "2D", "3D", "NA"]
 CURRENT_CHOICES = ["DC", "AC", "NA"]
 
-# ========================= Model Loader ================================================
+========================= Model Loader ================================================
 
 MODEL_CANDIDATES = [
-    "stress_gf_xgb.joblib",
-    "models/stress_gf_xgb.joblib",
-    "/home/user/app/stress_gf_xgb.joblib",
+"stress_gf_xgb.joblib",
+"models/stress_gf_xgb.joblib",
+"/home/user/app/stress_gf_xgb.joblib",
 ]
 
 def _load_model_or_error():
-    for p in MODEL_CANDIDATES:
-        if os.path.exists(p):
-            try:
-                return joblib.load(p)
-            except Exception as e:
-                return f"Could not load model from {p}: {e}"
-    return (
-        "Model file not found. Upload your trained pipeline as "
-        "stress_gf_xgb.joblib (or put it in models/)."
-    )
-
-# ========================= Input Coercion =============================================
+for p in MODEL_CANDIDATES:
+if os.path.exists(p):
+try:
+return joblib.load(p)
+except Exception as e:
+return f"Could not load model from {p}: {e}"
+return (
+"Model file not found. Upload your trained pipeline as "
+"stress_gf_xgb.joblib (or put it in models/)."
+)
+
+========================= Input Coercion =============================================
 
 def _coerce_to_row(form_dict: dict) -> pd.DataFrame:
-    """
-    Convert raw UI dict -> single-row DataFrame with columns MAIN_VARIABLES.
-    Numeric fields: float or NaN; categorical: stripped string (or empty).
-    Missing columns are filled with NaN/'' so the pipeline imputers can handle them.
-    """
-    row = {}
-    for col in MAIN_VARIABLES:
-        v = form_dict.get(col, None)
-        if col in NUMERIC_COLS:
-            if v in ("", None):
-                row[col] = np.nan
-            else:
-                try:
-                    row[col] = float(v)
-                except:
-                    row[col] = np.nan
-        else:
-            row[col] = "" if v in (None, "NA") else str(v).strip()
-    # Ensure exact column order
-    return pd.DataFrame([row], columns=MAIN_VARIABLES)
-
-# ========================= Predict Function ===========================================
+"""
+Convert raw UI dict -> single-row DataFrame with columns MAIN_VARIABLES.
+Numeric fields: float or NaN; categorical: stripped string (or empty).
+Missing columns are filled with NaN/'' so the pipeline imputers can handle them.
+"""
+row = {}
+for col in MAIN_VARIABLES:
+v = form_dict.get(col, None)
+if col in NUMERIC_COLS:
+if v in ("", None):
+row[col] = np.nan
+else:
+try:
+row[col] = float(v)
+except:
+row[col] = np.nan
+else:
+row[col] = "" if v in (None, "NA") else str(v).strip()
+# Ensure exact column order
+return pd.DataFrame([row], columns=MAIN_VARIABLES)
+
+========================= Predict Function ===========================================
 
 def predict_fn(**kwargs):
-    mdl = _load_model_or_error()
-    if isinstance(mdl, str):
-        # return a friendly error string
-        return mdl
+mdl = _load_model_or_error()
+if isinstance(mdl, str):
+# return a friendly error string
+return mdl
 
-    X_new = _coerce_to_row(kwargs)
+X_new = _coerce_to_row(kwargs)
 
-    try:
-        y_log = mdl.predict(X_new)  # model predicts log1p(target)
-        y = float(np.expm1(y_log)[0])  # back to original scale MPa^-1
-        # protect from tiny negative numeric noise
-        if -1e-10 < y < 0:
-            y = 0.0
-        return y
-    except Exception as e:
-        return f"Prediction error: {e}"
+try:
+    y_log = mdl.predict(X_new)  # model predicts log1p(target)
+    y = float(np.expm1(y_log)[0])  # back to original scale MPa^-1
+    # protect from tiny negative numeric noise
+    if -1e-10 < y < 0:
+        y = 0.0
+    return y
+except Exception as e:
+    return f"Prediction error: {e}"
 
-# ========================= Example Prefill ============================================
+========================= Example Prefill ============================================
 
 EXAMPLE = {
-    "Filler 1 Type": "CNT",
-    "Filler 1 Dimensionality": "1D",
-    "Filler 1 Diameter (µm)": 0.02,
-    "Filler 1 Length (mm)": 1.2,
-    CF_COL: 0.5,
-    "Filler 2 Type": "",
-    "Filler 2 Dimensionality": "NA",
-    "Filler 2 Diameter (µm)": None,
-    "Filler 2 Length (mm)": None,
-    "Specimen Volume (mm3)": 1000,
-    "Probe Count": 2,
-    "Probe Material": "Copper",
-    "W/B": 0.4,
-    "S/B": 2.5,
-    "Gauge Length (mm)": 20,
-    "Curing Condition": "28d water, 20°C",
-    "Number of Fillers": 1,
-    "Drying Temperature (°C)": 60,
-    "Drying Duration (hr)": 24,
-    "Loading Rate (MPa/s)": 0.1,
-    "Modulus of Elasticity (GPa)": 25,
-    "Current Type": "DC",
-    "Applied Voltage (V)": 5.0,
+"Filler 1 Type": "CNT",
+"Filler 1 Dimensionality": "1D",
+"Filler 1 Diameter (µm)": 0.02,
+"Filler 1 Length (mm)": 1.2,
+CF_COL: 0.5,
+"Filler 2 Type": "",
+"Filler 2 Dimensionality": "NA",
+"Filler 2 Diameter (µm)": None,
+"Filler 2 Length (mm)": None,
+"Specimen Volume (mm3)": 1000,
+"Probe Count": 2,
+"Probe Material": "Copper",
+"W/B": 0.4,
+"S/B": 2.5,
+"Gauge Length (mm)": 20,
+"Curing Condition": "28d water, 20°C",
+"Number of Fillers": 1,
+"Drying Temperature (°C)": 60,
+"Drying Duration (hr)": 24,
+"Loading Rate (MPa/s)": 0.1,
+"Modulus of Elasticity (GPa)": 25,
+"Current Type": "DC",
+"Applied Voltage (V)": 5.0,
 }
 
 def _fill_example():
-    return [EXAMPLE.get(k, None) for k in MAIN_VARIABLES]
+return [EXAMPLE.get(k, None) for k in MAIN_VARIABLES]
 
 def _clear_all():
-    # Return blanks in the same order as MAIN_VARIABLES
-    cleared = []
-    for col in MAIN_VARIABLES:
-        if col in NUMERIC_COLS:
-            cleared.append(None)
-        elif col in {"Filler 1 Dimensionality", "Filler 2 Dimensionality"}:
-            cleared.append("NA")
-        elif col == "Current Type":
-            cleared.append("NA")
-        else:
-            cleared.append("")
-    return cleared
-
-# ========================= UI =========================================================
+# Return blanks in the same order as MAIN_VARIABLES
+cleared = []
+for col in MAIN_VARIABLES:
+if col in NUMERIC_COLS:
+cleared.append(None)
+elif col in {"Filler 1 Dimensionality", "Filler 2 Dimensionality"}:
+cleared.append("NA")
+elif col == "Current Type":
+cleared.append("NA")
+else:
+cleared.append("")
+return cleared
+
+========================= UI =========================================================
 
 CSS = """
 /* blue→green background (replaces grayscale) */
 .gradio-container {
-  background:
-    radial-gradient(1200px 600px at 10% -10%, rgba(14,165,233,0.30), transparent 60%),
-    radial-gradient(1000px 500px at 110% 110%, rgba(16,185,129,0.28), transparent 55%),
-    linear-gradient(135deg,#0b1220 0%, #06231b 100%);
+background:
+radial-gradient(1200px 600px at 10% -10%, rgba(14,165,233,0.30), transparent 60%),
+radial-gradient(1000px 500px at 110% 110%, rgba(16,185,129,0.28), transparent 55%),
+linear-gradient(135deg,#0b1220 0%, #06231b 100%);
 }
 
 /* keep your font stack */
-* { font-family: ui-sans-serif, system-ui, -apple-system, 'Segoe UI', Roboto, 'Helvetica Neue', Arial; }
+
+{ font-family: ui-sans-serif, system-ui, -apple-system, 'Segoe UI', Roboto, 'Helvetica Neue', Arial; }
 
 /* cards */
 .card { background: rgba(255,255,255,0.05) !important; border: 1px solid rgba(255,255,255,0.10); }
 
 /* labels */
-label.svelte-1ipelgc { color: #dbeafe !important; }  /* light blue */
+label.svelte-1ipelgc { color: #dbeafe !important; } # light blue
 """
 
 theme = gr.themes.Soft(
-    primary_hue="sky",       # blue
-    secondary_hue="emerald", # green
-    neutral_hue="slate"
+primary_hue="sky", # blue
+secondary_hue="emerald", # green
+neutral_hue="slate"
 ).set(
-    body_background_fill="#0b1220",
-    body_text_color="#e6f1f5",
-    input_background_fill="#0f172a",
-    input_border_color="#1f2937",
-    button_primary_background_fill="#0284c7",  # blue
-    button_primary_text_color="#ffffff",
-    button_secondary_background_fill="#0b2a22" # deep teal
+body_background_fill="#0b1220",
+body_text_color="#e6f1f5",
+input_background_fill="#0f172a",
+input_border_color="#1f2937",
+button_primary_background_fill="#0284c7", # blue
+button_primary_text_color="#ffffff",
+button_secondary_background_fill="#0b2a22" # deep teal
 )
 
 with gr.Blocks(css=CSS, theme=theme, fill_height=True) as demo:
-    gr.Markdown(
-        "<h1 style='margin:0'>🔮 Stress Gauge Factor (MPa⁻¹) — ML Predictor</h1>"
-        "<p style='opacity:.9'>Fields and units match your training data. "
-        "Leave anything blank if unknown — the model handles missing values.</p>"
-    )
-
-    with gr.Row():
-        # ---------------- Inputs (Left) ----------------
-        with gr.Column(scale=7):
-            # Primary filler
-            with gr.Accordion("Primary conductive filler", open=True, elem_classes=["card"]):
-                f1_type = gr.Textbox(label="Filler 1 Type", placeholder="e.g., CNT, Graphite, Steel fiber")
-                f1_dim = gr.Dropdown(DIM_CHOICES, value="NA", label="Filler 1 Dimensionality")
-                f1_diam = gr.Number(label="Filler 1 Diameter (µm)")
-                f1_len = gr.Number(label="Filler 1 Length (mm)")
-                cf_conc = gr.Number(label=f"{CF_COL}", info="Weight percent of total binder")
-
-            # Secondary filler (optional)
-            with gr.Accordion("➕ Secondary filler (optional)", open=False, elem_classes=["card"]):
-                f2_type = gr.Textbox(label="Filler 2 Type", placeholder="Optional")
-                f2_dim = gr.Dropdown(DIM_CHOICES, value="NA", label="Filler 2 Dimensionality")
-                f2_diam = gr.Number(label="Filler 2 Diameter (µm)")
-                f2_len = gr.Number(label="Filler 2 Length (mm)")
-
-            # Mix & specimen
-            with gr.Accordion("Mix design & specimen", open=False, elem_classes=["card"]):
-                spec_vol = gr.Number(label="Specimen Volume (mm3)")
-                probe_cnt = gr.Number(label="Probe Count")
-                probe_mat = gr.Textbox(label="Probe Material", placeholder="e.g., Copper, Silver paste")
-                wb = gr.Number(label="W/B")
-                sb = gr.Number(label="S/B")
-                gauge_len = gr.Number(label="Gauge Length (mm)")
-                curing = gr.Textbox(label="Curing Condition", placeholder="e.g., 28d water, 20°C")
-                n_fillers = gr.Number(label="Number of Fillers")
-
-            # Processing
-            with gr.Accordion("Processing", open=False, elem_classes=["card"]):
-                dry_temp = gr.Number(label="Drying Temperature (°C)")
-                dry_hrs = gr.Number(label="Drying Duration (hr)")
-
-            # Mechanical & Electrical loading
-            with gr.Accordion("Mechanical & electrical loading", open=False, elem_classes=["card"]):
-                load_rate = gr.Number(label="Loading Rate (MPa/s)")
-                E_mod     = gr.Number(label="Modulus of Elasticity (GPa)")
-                current   = gr.Dropdown(CURRENT_CHOICES, value="NA", label="Current Type")
-                voltage   = gr.Number(label="Applied Voltage (V)")
-
-        # ---------------- Output (Right) ----------------
-        with gr.Column(scale=5):
-            with gr.Group(elem_classes=["card"]):
-                out_pred = gr.Number(label="Predicted Stress GF (MPa-1)", precision=6)
-                with gr.Row():
-                    btn_pred  = gr.Button("Predict", variant="primary")
-                    btn_clear = gr.Button("Clear")
-                    btn_demo  = gr.Button("Fill Example")
-
-            with gr.Accordion("About this model", open=False, elem_classes=["card"]):
-                gr.Markdown(
-                    "- Pipeline: **ColumnTransformer → (RobustScaler + OneHot) → XGBoost**\n"
-                    "- Target: **Stress GF (MPa⁻¹)** on original scale (model trains on log1p).\n"
-                    "- Missing values are safely imputed per-feature.\n"
-                    "- Trained columns:\n"
-                    f"  `{', '.join(MAIN_VARIABLES)}`"
-                )
-
-    # Wire buttons
-    inputs_in_order = [
-        # MAIN_VARIABLES exact order:
-        # "Filler 1 Type","Filler 1 Diameter (µm)","Filler 1 Length (mm)", CF_COL,
-        # "Filler 1 Dimensionality","Filler 2 Type","Filler 2 Diameter (µm)","Filler 2 Length (mm)",
-        # "Filler 2 Dimensionality","Specimen Volume (mm3)","Probe Count","Probe Material",
-        # "W/B","S/B","Gauge Length (mm)","Curing Condition","Number of Fillers",
-        # "Drying Temperature (°C)","Drying Duration (hr)","Loading Rate (MPa/s)",
-        # "Modulus of Elasticity (GPa)","Current Type","Applied Voltage (V)"
-        f1_type, f1_diam, f1_len, cf_conc,
-        f1_dim,  f2_type, f2_diam, f2_len,
-        f2_dim,  spec_vol, probe_cnt, probe_mat,
-        wb, sb, gauge_len, curing, n_fillers,
-        dry_temp, dry_hrs, load_rate,
-        E_mod, current, voltage
-    ]
-
-    def _predict_wrapper(*vals):
-        data = {k: v for k, v in zip(MAIN_VARIABLES, vals)}
-        return predict_fn(**data)
-
-    btn_pred.click(_predict_wrapper, inputs=inputs_in_order, outputs=out_pred)
-    btn_clear.click(lambda: _clear_all(), inputs=None, outputs=inputs_in_order)
-    btn_demo.click(lambda: _fill_example(), inputs=None, outputs=inputs_in_order)
-
-# ------------- Launch -------------
-if __name__ == "__main__":
-    demo.queue().launch()
-```
+gr.Markdown(
+"<h1 style='margin:0'>🔮 Stress Gauge Factor (MPa⁻¹) — ML Predictor</h1>"
+"<p style='opacity:.9'>Fields and units match your training data. "
+"Leave anything blank if unknown — the model handles missing values.</p>"
+)
+
+with gr.Row():
+    # ---------------- Inputs (Left) ----------------
+    with gr.Column(scale=7):
+        # Primary filler
+        with gr.Accordion("Primary conductive filler", open=True, elem_classes=["card"]):
+            f1_type = gr.Textbox(label="Filler 1 Type", placeholder="e.g., CNT, Graphite, Steel fiber")
+            f1_dim = gr.Dropdown(DIM_CHOICES, value="NA", label="Filler 1 Dimensionality")
+            f1_diam = gr.Number(label="Filler 1 Diameter (µm)")
+            f1_len = gr.Number(label="Filler 1 Length (mm)")
+            cf_conc = gr.Number(label=f"{CF_COL}", info="Weight percent of total binder")
+
+        # Secondary filler (optional)
+        with gr.Accordion("➕ Secondary filler (optional)", open=False, elem_classes=["card"]):
+            f2_type = gr.Textbox(label="Filler 2 Type", placeholder="Optional")
+            f2_dim = gr.Dropdown(DIM_CHOICES, value="NA", label="Filler 2 Dimensionality")
+            f2_diam = gr.Number(label="Filler 2 Diameter (µm)")
+            f2_len = gr.Number(label="Filler 2 Length (mm)")
+
+        # Mix & specimen
+        with gr.Accordion("Mix design & specimen", open=False, elem_classes=["card"]):
+            spec_vol = gr.Number(label="Specimen Volume (mm3)")
+            probe_cnt = gr.Number(label="Probe Count")
+            probe_mat = gr.Textbox(label="Probe Material", placeholder="e.g., Copper, Silver paste")
+            wb = gr.Number(label="W/B")
+            sb = gr.Number(label="S/B")
+            gauge_len = gr.Number(label="Gauge Length (mm)")
+            curing = gr.Textbox(label="Curing Condition", placeholder="e.g., 28d water, 20°C")
+            n_fillers = gr.Number(label="Number of Fillers")
+
+        # Processing
+        with gr.Accordion("Processing", open=False, elem_classes=["card"]):
+            dry_temp = gr.Number(label="Drying Temperature (°C)")
+            dry_hrs = gr.Number(label="Drying Duration (hr)")
+
+        # Mechanical & Electrical loading
+        with gr.Accordion("Mechanical & electrical loading", open=False, elem_classes=["card"]):
+            load_rate = gr.Number(label="Loading Rate (MPa/s)")
+            E_mod     = gr.Number(label="Modulus of Elasticity (GPa)")
+            current   = gr.Dropdown(CURRENT_CHOICES, value="NA", label="Current Type")
+            voltage   = gr.Number(label="Applied Voltage (V)")
+
+    # ---------------- Output (Right) ----------------
+    with gr.Column(scale=5):
+        with gr.Group(elem_classes=["card"]):
+            out_pred = gr.Number(label="Predicted Stress GF (MPa-1)", precision=6)
+            with gr.Row():
+                btn_pred  = gr.Button("Predict", variant="primary")
+                btn_clear = gr.Button("Clear")
+                btn_demo  = gr.Button("Fill Example")
+
+        with gr.Accordion("About this model", open=False, elem_classes=["card"]):
+            gr.Markdown(
+                "- Pipeline: **ColumnTransformer → (RobustScaler + OneHot) → XGBoost**\n"
+                "- Target: **Stress GF (MPa⁻¹)** on original scale (model trains on log1p).\n"
+                "- Missing values are safely imputed per-feature.\n"
+                "- Trained columns:\n"
+                f"  `{', '.join(MAIN_VARIABLES)}`"
+            )
+
+# Wire buttons
+inputs_in_order = [
+    # MAIN_VARIABLES exact order:
+    # "Filler 1 Type","Filler 1 Diameter (µm)","Filler 1 Length (mm)", CF_COL,
+    # "Filler 1 Dimensionality","Filler 2 Type","Filler 2 Diameter (µm)","Filler 2 Length (mm)",
+    # "Filler 2 Dimensionality","Specimen Volume (mm3)","Probe Count","Probe Material",
+    # "W/B","S/B","Gauge Length (mm)","Curing Condition","Number of Fillers",
+    # "Drying Temperature (°C)","Drying Duration (hr)","Loading Rate (MPa/s)",
+    # "Modulus of Elasticity (GPa)","Current Type","Applied Voltage (V)"
+    f1_type, f1_diam, f1_len, cf_conc,
+    f1_dim,  f2_type, f2_diam, f2_len,
+    f2_dim,  spec_vol, probe_cnt, probe_mat,
+    wb, sb, gauge_len, curing, n_fillers,
+    dry_temp, dry_hrs, load_rate,
+    E_mod, current, voltage
+]
+
+def _predict_wrapper(*vals):
+    data = {k: v for k, v in zip(MAIN_VARIABLES, vals)}
+    return predict_fn(**data)
+
+btn_pred.click(_predict_wrapper, inputs=inputs_in_order, outputs=out_pred)
+btn_clear.click(lambda: _clear_all(), inputs=None, outputs=inputs_in_order)
+btn_demo.click(lambda: _fill_example(), inputs=None, outputs=inputs_in_order)